CriticalHop Kubernetes Failure Prevention

Why CriticalHop ?

CriticalHop is helping you minimize Kubernetes issues by running autonomous checks, config validations and predicting other risks before they affect your production environment. Our AI heuristic navigates through a model of possible actions of events that could lead to an outage by modeling the outcomes of your changes and by applying our growing knowledge of config scenarios.

Alteranatives

The current alternative is assigning a production engineer who would take the responsibility for one or more services. Synchronizing a team of production engineers may be a challenge, and they are usually assigned for after-the-fact outage analysis and debugging. To prevent potential failure scenarios, many organizations turn to creating prohibitive policies all over the cluster that inevitably lead to slowing down the process and reduces benefits of a new architecture.

Key Features

Development

Instantly figure out risks with new configs while developing a new microservice

Deployment

Get immediate config validation for your Kubernetes cluster

Multi-cloud

Solve your cloud infrastructure connectivity or hybrid network services issues.

Monitoring

Get solution to a problem before you know it. Security included.

Great Support

We always glad to help, so don’t hesitate to contact us if you have any questions or suggestions about CriticalHop kubernetes debugger.

Benefits

Minimized failures due to the way Autoscaler behaves with all of the above – e.g. simulated load on specific service may trigger random evictions, freezes, or fail to perform scaling due to constraints/missing nodes that have requested resources.

Minimized failures due to resource limits mistakes. Various scenarios with setting incorrect constraints of limits/requests/taints/affinity – e.g. creating a DaemonSet with limits bound to nodes with pods with no limits will block the cluster from starting any pods at all; and will evict and kill the cluster if podPriority is set to “critical” (we have a full model of RAM/CPU resources that covers all possible variations)

Minimized failures due to misconfigured Cron jobs. Cases like when the job takes longer to complete than it is scheduled to run with no/incorrect concurrency Policy set, combined with all of the eviction/freeze scenarios with the above.

Minimized failures due to resource utilization mishandling. Various scenarios covered, when you have too many idling pods on a node with a history of consuming lots of RAM, or when you forget to define appropriate anti-affinity – e.g. you create a new job, the pod gets scheduled on a machine with high RAM utilization, renders node unusable, gets restarted on new node, kills that too, etc.... (we have a full model of restart policy and real resource consumption that is getting filled with linked data when available).

Minimized failures due to incorrect image pull policies (static, in the process of expanding to dynamic model).These benefits are available right away and they are modeled in our AI planning domain that covers substantially more failure scenarios than is possible by manually writing each policy.

What People Say About Us

"The capabilities of the AI planning in Enterprise is unmatched, and guys at CriticalHop have proven that automation with commercial AI planning is unavoidable."